{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9f317959",
   "metadata": {},
   "source": [
    "# THE CODE IS IN MY GITHUB!!!\n",
    "Go and checkout my github if you want the to_candle file!\n",
    "## https://github.com/kecoma1/Trading_BOT\n",
    "https://github.com/kecoma1/Trading_BOT/tree/main/ta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c7c9da56",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import to_candle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6bc21d4",
   "metadata": {},
   "source": [
    "## Candle dataframe\n",
    "This dataframe is obtained through the from_tick_to_candle function. The dataframe contains the following columns:\n",
    "* **TIME-UTC**: UTC-TIME since epoch in seconds.\n",
    "* **OPEN**: Open price of that candle.\n",
    "* **CLOSE**: Close price of the candle.\n",
    "* **HIGH**: Highest price of the candle.\n",
    "* **LOW**: Lowest price of the candle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "da2c78d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TIME-UTC</th>\n",
       "      <th>OPEN</th>\n",
       "      <th>CLOSE</th>\n",
       "      <th>HIGH</th>\n",
       "      <th>LOW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.632953e+09</td>\n",
       "      <td>1.15982</td>\n",
       "      <td>1.15968</td>\n",
       "      <td>1.16015</td>\n",
       "      <td>1.15939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.632954e+09</td>\n",
       "      <td>1.15968</td>\n",
       "      <td>1.15983</td>\n",
       "      <td>1.15990</td>\n",
       "      <td>1.15964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.632955e+09</td>\n",
       "      <td>1.15983</td>\n",
       "      <td>1.15966</td>\n",
       "      <td>1.15983</td>\n",
       "      <td>1.15964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.632956e+09</td>\n",
       "      <td>1.15966</td>\n",
       "      <td>1.15980</td>\n",
       "      <td>1.15997</td>\n",
       "      <td>1.15966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.632956e+09</td>\n",
       "      <td>1.15980</td>\n",
       "      <td>1.15975</td>\n",
       "      <td>1.15990</td>\n",
       "      <td>1.15966</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       TIME-UTC     OPEN    CLOSE     HIGH      LOW\n",
       "0  1.632953e+09  1.15982  1.15968  1.16015  1.15939\n",
       "1  1.632954e+09  1.15968  1.15983  1.15990  1.15964\n",
       "2  1.632955e+09  1.15983  1.15966  1.15983  1.15964\n",
       "3  1.632956e+09  1.15966  1.15980  1.15997  1.15966\n",
       "4  1.632956e+09  1.15980  1.15975  1.15990  1.15966"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candle_df = to_candle.from_tick_to_candle(\"EURUSD.csv\", 15*60)\n",
    "candle_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8387110",
   "metadata": {},
   "source": [
    "## Technical analysis library - ta.trends.MACD\n",
    "**pip install ta**\n",
    "\n",
    "Using the *ta* library we are going to load the MACD information of the current dataframe using the *close column*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1cb156c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ta.trend import MACD\n",
    "macd_object = MACD(candle_df[\"CLOSE\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8a7846c",
   "metadata": {},
   "source": [
    "Computing the MACD line and the SIGNAL line.\n",
    "\n",
    "### MACD Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "55ef66b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "178    0.000187\n",
       "179    0.000190\n",
       "180    0.000180\n",
       "181    0.000196\n",
       "182    0.000199\n",
       "Name: MACD_12_26, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "macd_line = macd_object.macd()\n",
    "macd_line.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa764ac6",
   "metadata": {},
   "source": [
    "### SIGNAL Line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3b3afc04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "178    0.000234\n",
       "179    0.000225\n",
       "180    0.000216\n",
       "181    0.000212\n",
       "182    0.000210\n",
       "Name: MACD_sign_12_26, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "signal_line = macd_object.macd_signal()\n",
    "signal_line.tail(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac9ffa1c",
   "metadata": {},
   "source": [
    "# PLOTS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "21c3e98c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "macd_line.plot(ax=signal_line.plot())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
